---
title: 'Self-Consistency'
description: 'Learn how to implement self-consistency to improve AI reasoning reliability through multiple sampling and majority voting'
---

## What is Self-Consistency?

Self-consistency is a prompting technique that improves the reliability of AI reasoning by generating multiple responses to the same question and then selecting the most consistent answer through majority voting. Unlike traditional Chain-of-Thought prompting that uses greedy decoding for a single reasoning path, self-consistency leverages diverse sampling to explore multiple reasoning perspectives before converging on the most reliable answer.

## Why Use Self-Consistency?

- **Improved Accuracy**: Multiple samples reduce the impact of random errors and greedy decoding limitations
- **Better Reasoning**: Helps identify the most logical solution path from diverse perspectives
- **Reduced Hallucinations**: Inconsistent responses are filtered out through majority voting
- **Confidence Assessment**: Provides pseudo-probability likelihood of answer correctness
- **Complex Problem Solving**: Particularly effective for math, logic, and multi-step reasoning where single attempts may fail
- **Robust Decision Making**: Overcomes limitations of single reasoning paths in ambiguous scenarios

## Basic Implementation in Latitude

Here's a simple self-consistency example for classification tasks:

```markdown Classification with Self-Consistency
---
provider: OpenAI
model: gpt-4o
temperature: 0.7
---

# Content Classification

Classify the following content and explain your reasoning step by step.

## Content:
{{ content_to_classify }}

## Classification Process:
Let me analyze this step by step:

1. **Content Analysis:**
   - What type of content is this?
   - What are the key indicators?

2. **Context Evaluation:**
   - What contextual clues are present?
   - How do tone and language affect classification?

3. **Risk Assessment:**
   - What potential impacts should be considered?
   - Are there any warning signs?

4. **Final Classification:**
   Based on my analysis: [CATEGORY]

**Reasoning:** [Detailed explanation of decision]
```

## How Self-Consistency Works

The self-consistency process follows three key steps:

1. **Diverse Path Generation**: The same prompt is submitted multiple times with higher temperature settings (0.6-0.8) to encourage different reasoning approaches and perspectives
2. **Answer Extraction**: Each response is analyzed to extract the core answer or classification, regardless of the reasoning path taken
3. **Majority Voting**: The most frequently occurring answer across all samples is selected as the final result

This approach provides a form of confidence scoring - answers that appear consistently across multiple reasoning paths are more likely to be correct than those that appear only once.

## Advanced Implementation with Multiple Samples

Let's create a more sophisticated example that uses Latitude's chain feature to generate and compare multiple reasoning paths:

<CodeGroup>
```markdown Advanced Self-Consistency
---
provider: OpenAI
model: gpt-4o
temperature: 0.8
---

<step>
# Reasoning Sample 1

Solve this problem using your preferred approach:

## Problem:
{{ reasoning_problem }}

## Solution Path 1:
Think through this step by step and provide your final answer.
</step>

<step>
# Reasoning Sample 2

Solve the same problem using a different approach if possible:

## Problem:
{{ reasoning_problem }}

## Solution Path 2:
Think through this step by step and provide your final answer.
</step>

<step>
# Reasoning Sample 3

Solve the problem one more time, focusing on accuracy:

## Problem:
{{ reasoning_problem }}

## Solution Path 3:
Think through this step by step and provide your final answer.
</step>

<step>
# Self-Consistency Analysis

Review the three solution paths above and determine the most consistent answer:

## Analysis:
1. **Compare the final answers:** Are they the same or different?
2. **Evaluate reasoning quality:** Which path has the most sound logic?
3. **Identify consensus:** What answer appears most frequently?

## Final Consistent Answer:
Based on the analysis above, the most reliable answer is:

**Answer:**
**Confidence Level:**
**Reasoning:**
</step>
```
</CodeGroup>

In this advanced example:

1. **Multiple Sampling**: We generate three independent solutions with higher temperature for diversity
2. **Chain Processing**: Each step builds on the previous ones for comparison
3. **Consistency Analysis**: A final step evaluates and selects the best answer
4. **Confidence Assessment**: The system provides a confidence level based on agreement

## Logic and Reasoning Self-Consistency

Use self-consistency for complex logical problems:

<CodeGroup>
```markdown Logic Problem Self-Consistency
---
provider: OpenAI
model: gpt-4o
temperature: 0.6
---

<step>
# Deductive Reasoning Approach

Solve this logic problem using deductive reasoning:

## Problem:
{{ logic_problem }}

## Deductive Solution:
Start with the given facts and work logically to the conclusion:

1. **Given facts:**
2. **Logical deductions:**
3. **Conclusion:**
</step>

<step>
# Inductive Reasoning Approach

Solve the same problem using inductive reasoning:

## Problem:
{{ logic_problem }}

## Inductive Solution:
Look for patterns and make generalizations:

1. **Observe patterns:**
2. **Form hypothesis:**
3. **Test and conclude:**
</step>

<step>
# Abductive Reasoning Approach

Solve using abductive reasoning (inference to best explanation):

## Problem:
{{ logic_problem }}

## Abductive Solution:
Find the most likely explanation:

1. **Observations:**
2. **Possible explanations:**
3. **Best explanation:**
</step>

# Logic Consensus

Compare all three reasoning approaches

## Consensus Analysis:
- **Agreement level:** Do all approaches reach the same conclusion?
- **Strongest reasoning:** Which approach provides the most convincing logic?
- **Consistency score:** How well do the results align?

## Final Answer:
```
</CodeGroup>

## Multi-Agent Self-Consistency

Combine self-consistency with Latitude's agent system for specialized reasoning:

<CodeGroup>
```markdown Multi-Agent Self-Consistency
---
provider: OpenAI
model: gpt-4o
temperature: 0.5
type: agent
agents:
  - agents/mathematician
  - agents/logician
  - agents/analyst
---

# Multi-Expert Self-Consistency

Get multiple expert opinions and find the consensus:

## Problem:
{{ complex_problem }}

## Expert Consultation:
1. **Mathematician**: Analyze from a mathematical perspective
2. **Logician**: Apply formal logical reasoning
3. **Analyst**: Use analytical problem-solving methods

Coordinate with all experts and provide a self-consistent final answer.

```

```markdown agents/mathematician
---
provider: OpenAI
model: gpt-4o
temperature: 0.4
type: agent
---

# Mathematics Expert

I am a mathematics expert specializing in problem-solving with rigorous mathematical methods.

## Problem Analysis:
{{ complex_problem }}

## Mathematical Approach:
1. **Identify mathematical concepts involved**
2. **Apply relevant formulas and theorems**
3. **Show detailed calculations**
4. **Verify results through alternative methods**

## Mathematical Solution:
```

```markdown agents/logician
---
provider: OpenAI
model: gpt-4o
temperature: 0.4
type: agent
---

# Logic Expert

I am a logic expert specializing in formal reasoning and logical analysis.

## Problem Analysis:
{{ complex_problem }}

## Logical Approach:
1. **Structure the problem logically**
2. **Identify premises and conclusions**
3. **Apply logical rules and principles**
4. **Check for logical consistency**

## Logical Solution:
```

```markdown agents/analyst
---
provider: OpenAI
model: gpt-4o
temperature: 0.4
type: agent
---

# General Analyst

I am a general analyst specializing in systematic problem-solving and critical thinking.

## Problem Analysis:
{{ complex_problem }}

## Analytical Approach:
1. **Break down the problem systematically**
2. **Consider multiple perspectives**
3. **Evaluate evidence and assumptions**
4. **Synthesize findings**

## Analytical Solution:
```
</CodeGroup>

## Best Practices for Self-Consistency

<AccordionGroup>
<Accordion title="Sample Generation">
**Optimal Sampling**:
- Use 3-5 samples for most problems (balance cost vs. accuracy)
- Increase temperature (0.6-0.8) to encourage diverse reasoning paths and overcome greedy decoding
- Ensure each sample approaches the problem independently
- Vary the prompt slightly to encourage different analytical perspectives

**Quality Control**:
- Generate enough samples to identify patterns
- Filter out obviously flawed reasoning
- Weight samples based on reasoning quality, not just frequency
- Consider partial agreements in complex problems
</Accordion>

<Accordion title="Consistency Analysis">
**Evaluation Criteria**:
- **Answer consistency**: Do multiple samples reach the same conclusion?
- **Reasoning quality**: Which reasoning paths are most sound?
- **Method diversity**: Are different valid approaches represented?
- **Confidence indicators**: How certain can we be about the consensus?

**Analysis Techniques**:
- Majority voting for clear disagreements
- Weighted voting based on reasoning quality
- Partial credit for answers that are close but not identical
- Meta-reasoning about why inconsistencies occur
</Accordion>

<Accordion title="Problem Selection">
**Best Use Cases**:
- Classification tasks with potential ambiguity
- Mathematical word problems
- Logical reasoning puzzles
- Multi-step analytical tasks
- Questions with clear right/wrong answers where reasoning path matters
- Security-sensitive decisions requiring high confidence

**Less Suitable Cases**:
- Creative writing tasks
- Subjective opinion questions
- Simple factual lookups
- Tasks requiring consistent style/voice
</Accordion>

<Accordion title="Performance Optimization">
**Efficiency Tips**:
- Use parallel processing when possible
- Cache common problem types
- Implement early stopping if consensus is clear
- Balance sample count with accuracy needs

**Cost Management**:
- Start with fewer samples and increase if needed based on consistency scores
- Use cheaper models for initial sampling, better models for final analysis
- Implement confidence thresholds to determine optimal sample count
- Consider the cost trade-off: higher accuracy vs. increased computational expense
- Remember that self-consistency has high costs but provides pseudo-probability confidence
</Accordion>
</AccordionGroup>

## Advanced Techniques

### Adaptive Self-Consistency

Create prompts that adjust based on initial consistency. You can [play with it here](https://app.latitude.so/share/d/2a66d7c5-841d-4217-81cb-f97610ac9374)

<CodeGroup>
```markdown Adaptive Self-Consistency
---
provider: OpenAI
model: gpt-4.1-mini
temperature: 0.7
---

<step>
# Initial Sample Generation

Generate 3 initial solutions:

## Problem: {{ problem }}

### Solution 1:
### Solution 2:
### Solution 3:
</step>
<step as="consistency_check" schema={{{type: "object", properties: {additional_samples: {type: "boolean"}}, required: ["additional_samples"]}}}>
# Check Initial Consistency

  Evaluate the consistency of initial samples

## Consistency Analysis:
  - Are the answers consistent? (Yes/No)
  - Confidence level in consensus: (1-10)
  - Need for additional samples: (Yes/No)

## Decision:
  If consistency is low (< 7/10), recommend generating 2-3 additional samples.
  If consistency is high (≥ 7/10), proceed with current consensus.
</step>

{{ if consistency_check.additional_samples }}
  <step>
    Generate 2 more solutions using different approaches:

    ## Problem: {{ problem }}

    ### Solution 4:
    ### Solution 5:

  </step>
{{ endif }}

# Final Consensus
Based on all available samples, determine the final answer:

## Final Self-Consistent Answer:
```
</CodeGroup>
<Note>Note how we used [structured outputs](/guides/prompt-manager/json-output) to capture the consistency check results and decide whether to generate additional samples.</Note>

### Self-Consistency with Uncertainty Quantification

Implement self-consistency that quantifies uncertainty:

<CodeGroup>
```markdown Uncertainty-Aware Self-Consistency
---
provider: OpenAI
model: gpt-4o
temperature: 0.8
---

<step>
# Generate Diverse Solutions

Create 5 solutions with different reasoning strategies:

## Problem: {{ problem }}

### Strategy 1 - Direct Approach:
### Strategy 2 - Step-by-step Breakdown:
### Strategy 3 - Alternative Method:
### Strategy 4 - Verification Focus:
### Strategy 5 - Edge Case Consideration:
</step>

<step>
# Uncertainty Quantification

Analyze the uncertainty in our solutions

## Uncertainty Assessment:
1. **Answer Distribution**: What answers appeared and how often?
2. **Reasoning Confidence**: How confident was each reasoning path?
3. **Method Agreement**: Do different methods agree?
4. **Edge Case Handling**: How well are corner cases addressed?

## Uncertainty Metrics:
- **Consensus Strength**: (0-100%)
- **Reasoning Diversity**: (Low/Medium/High)
- **Confidence Interval**: (if applicable)
- **Uncertainty Sources**: (List main sources of disagreement)

## Final Answer with Uncertainty:
**Most Likely Answer:**
**Confidence Level:**
**Alternative Possibilities:**
**Key Uncertainties:**
</step>
```
</CodeGroup>

## Integration with Other Techniques

Self-consistency works well combined with other prompting techniques:

- **Chain-of-Thought + Self-Consistency**: Generate multiple detailed reasoning chains to overcome greedy decoding limitations
- **Few-Shot + Self-Consistency**: Use examples to guide consistent reasoning patterns across multiple samples
- **Role-Playing + Self-Consistency**: Have different expert personas solve the same problem independently
- **Iterative Refinement + Self-Consistency**: Use consensus to improve solution quality through multiple rounds

The key is to maintain the core principle: generate multiple independent solutions and use agreement as a signal of reliability, while addressing the inherent limitations of single-path reasoning.

## Related Techniques

Explore these complementary prompting techniques to enhance your AI applications:

- **[Chain-of-Thought](./chain-of-thought)** - Break down complex problems into step-by-step reasoning
- **[Tree-of-Thoughts](./tree-of-thoughts)** - Explore multiple reasoning paths systematically
- **[Few-Shot Learning](./few-shot-learning)** - Use examples to guide AI behavior and improve consistency
